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AWARe effectively preserves prior knowledge in MLLMs while boosting performance on new tasks, challenging the conventional trade-off between retention and adaptation.
Saliency-driven prototype alignment can dramatically enhance dataset distillation, yielding better generalization and performance in complex visual tasks.
